{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "34a2118f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wfdb in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (4.3.0)\n",
      "Requirement already satisfied: aiohttp>=3.10.11 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from wfdb) (3.12.15)\n",
      "Requirement already satisfied: fsspec>=2023.10.0 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from wfdb) (2025.7.0)\n",
      "Requirement already satisfied: matplotlib>=3.2.2 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from wfdb) (3.10.1)\n",
      "Requirement already satisfied: numpy>=1.26.4 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from wfdb) (2.0.2)\n",
      "Requirement already satisfied: pandas>=2.2.3 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from wfdb) (2.3.1)\n",
      "Requirement already satisfied: requests>=2.8.1 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from wfdb) (2.28.2)\n",
      "Requirement already satisfied: scipy>=1.13.0 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from wfdb) (1.15.2)\n",
      "Requirement already satisfied: soundfile>=0.10.0 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from wfdb) (0.13.1)\n",
      "Requirement already satisfied: aiohappyeyeballs>=2.5.0 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from aiohttp>=3.10.11->wfdb) (2.6.1)\n",
      "Requirement already satisfied: aiosignal>=1.4.0 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from aiohttp>=3.10.11->wfdb) (1.4.0)\n",
      "Requirement already satisfied: attrs>=17.3.0 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from aiohttp>=3.10.11->wfdb) (25.3.0)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from aiohttp>=3.10.11->wfdb) (1.7.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from aiohttp>=3.10.11->wfdb) (6.6.4)\n",
      "Requirement already satisfied: propcache>=0.2.0 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from aiohttp>=3.10.11->wfdb) (0.3.2)\n",
      "Requirement already satisfied: yarl<2.0,>=1.17.0 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from aiohttp>=3.10.11->wfdb) (1.20.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib>=3.2.2->wfdb) (1.3.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib>=3.2.2->wfdb) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib>=3.2.2->wfdb) (4.57.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib>=3.2.2->wfdb) (1.4.8)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib>=3.2.2->wfdb) (24.2)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib>=3.2.2->wfdb) (11.2.1)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib>=3.2.2->wfdb) (3.2.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib>=3.2.2->wfdb) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas>=2.2.3->wfdb) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas>=2.2.3->wfdb) (2025.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests>=2.8.1->wfdb) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests>=2.8.1->wfdb) (3.6)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests>=2.8.1->wfdb) (1.26.18)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from requests>=2.8.1->wfdb) (2024.2.2)\n",
      "Requirement already satisfied: cffi>=1.0 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from soundfile>=0.10.0->wfdb) (1.17.1)\n",
      "Requirement already satisfied: typing-extensions>=4.2 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from aiosignal>=1.4.0->aiohttp>=3.10.11->wfdb) (4.12.2)\n",
      "Requirement already satisfied: pycparser in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from cffi>=1.0->soundfile>=0.10.0->wfdb) (2.22)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\akash\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=3.2.2->wfdb) (1.17.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 25.0.1 -> 25.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "pip install wfdb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c310835d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total waveform records found: 444\n",
      "\n",
      "Reading record: E:\\DL-BSP-SEMMM5\\data\\mimic3_waveform\\p000020\\3544749_0001\n",
      "Record Name: 3544749_0001\n",
      "Sampling Frequency: 125 Hz\n",
      "Signal Names: ['II', 'AVF', 'ABP', 'PAP']\n",
      "Number of Samples: 3811\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import wfdb\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Path to your MIMIC-III waveform data folder\n",
    "base_dir = r\"E:\\DL-BSP-SEMMM5\\data\\mimic3_waveform\"\n",
    "\n",
    "# Collect all .hea files recursively\n",
    "hea_files = []\n",
    "for root, dirs, files in os.walk(base_dir):\n",
    "    for file in files:\n",
    "        if file.endswith(\".hea\"):\n",
    "            hea_files.append(os.path.join(root, file))\n",
    "\n",
    "# Check how many records found\n",
    "print(f\"Total waveform records found: {len(hea_files)}\")\n",
    "\n",
    "# If no records found, stop execution\n",
    "if not hea_files:\n",
    "    raise FileNotFoundError(\"No waveform records found. Check your path!\")\n",
    "\n",
    "# Pick the first waveform record for visualization\n",
    "record_path = hea_files[0].replace(\".hea\", \"\")  # Remove .hea extension for wfdb\n",
    "print(f\"\\nReading record: {record_path}\")\n",
    "\n",
    "# Read the waveform using wfdb\n",
    "record = wfdb.rdrecord(record_path)\n",
    "\n",
    "# Display basic info about the record\n",
    "print(f\"Record Name: {record.record_name}\")\n",
    "print(f\"Sampling Frequency: {record.fs} Hz\")\n",
    "print(f\"Signal Names: {record.sig_name}\")\n",
    "print(f\"Number of Samples: {record.sig_len}\")\n",
    "\n",
    "# Plot all signals in the record\n",
    "wfdb.plot_wfdb(record=record, title=f\"Waveforms - {record.record_name}\")\n",
    "\n",
    "# Optionally, plot individual signals using matplotlib\n",
    "plt.figure(figsize=(12, 6))\n",
    "for i, sig in enumerate(record.p_signal.T):\n",
    "    plt.plot(sig, label=record.sig_name[i])\n",
    "plt.title(f\"Waveform Signals for {record.record_name}\")\n",
    "plt.xlabel(\"Samples\")\n",
    "plt.ylabel(\"Amplitude\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "37d6839c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total waveform records found: 444\n",
      "\n",
      "Found ECG/PPG signals in record: 3544749_0001\n",
      "Available signals: ['II', 'AVF', 'ABP', 'PAP']\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import wfdb\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Path to your MIMIC-III waveform data folder\n",
    "base_dir = r\"E:\\DL-BSP-SEMMM5\\data\\mimic3_waveform\"\n",
    "\n",
    "# Recursively find all .hea files\n",
    "hea_files = []\n",
    "for root, dirs, files in os.walk(base_dir):\n",
    "    for file in files:\n",
    "        if file.endswith(\".hea\"):\n",
    "            hea_files.append(os.path.join(root, file))\n",
    "\n",
    "print(f\"Total waveform records found: {len(hea_files)}\")\n",
    "if len(hea_files) == 0:\n",
    "    raise FileNotFoundError(\"⚠️ No waveform files found!\")\n",
    "\n",
    "# Function to check if ECG or PPG signals exist\n",
    "def has_ecg_or_ppg(record):\n",
    "    ecg_keywords = [\"ECG\", \"II\", \"I\", \"V1\", \"V2\", \"V3\", \"V4\", \"V5\", \"V6\", \"AVF\", \"AVL\", \"AVR\"]\n",
    "    ppg_keywords = [\"PLETH\", \"PPG\", \"IRPLETH\"]\n",
    "    ecg_indices = [i for i, name in enumerate(record.sig_name) if any(k in name.upper() for k in ecg_keywords)]\n",
    "    ppg_indices = [i for i, name in enumerate(record.sig_name) if any(k in name.upper() for k in ppg_keywords)]\n",
    "    return ecg_indices, ppg_indices\n",
    "\n",
    "# Iterate through all records until we find ECG or PPG signals\n",
    "for idx, hea_path in enumerate(hea_files):\n",
    "    record_path = hea_path[:-4]  # Remove \".hea\"\n",
    "    try:\n",
    "        record = wfdb.rdrecord(record_path)\n",
    "        ecg_indices, ppg_indices = has_ecg_or_ppg(record)\n",
    "\n",
    "        # If we find ECG or PPG, plot them\n",
    "        if ecg_indices or ppg_indices:\n",
    "            print(f\"\\nFound ECG/PPG signals in record: {record.record_name}\")\n",
    "            print(f\"Available signals: {record.sig_name}\")\n",
    "\n",
    "            plt.figure(figsize=(15, 6))\n",
    "\n",
    "            # Plot ECG if available\n",
    "            for i in ecg_indices:\n",
    "                plt.plot(record.p_signal[:, i], label=f\"ECG - {record.sig_name[i]}\")\n",
    "\n",
    "            # Plot PPG if available\n",
    "            for i in ppg_indices:\n",
    "                plt.plot(record.p_signal[:, i], label=f\"PPG - {record.sig_name[i]}\")\n",
    "\n",
    "            plt.title(f\"ECG & PPG Waveforms - {record.record_name}\")\n",
    "            plt.xlabel(\"Samples\")\n",
    "            plt.ylabel(\"Amplitude\")\n",
    "            plt.legend()\n",
    "            plt.show()\n",
    "            break  # Stop after finding the first valid record\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"⚠️ Error reading {record_path}: {e}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26451774",
   "metadata": {},
   "outputs": [
    {
     "ename": "PermissionError",
     "evalue": "[Errno 13] Permission denied: 'E:\\\\DL-BSP-SEMMM5\\\\data\\\\DIAGNOSES_ICD.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mPermissionError\u001b[39m                           Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[15]\u001b[39m\u001b[32m, line 18\u001b[39m\n\u001b[32m     16\u001b[39m admissions = pd.read_csv(os.path.join(BASE_DIR, \u001b[33m\"\u001b[39m\u001b[33madmissions.csv\u001b[39m\u001b[33m\"\u001b[39m))\n\u001b[32m     17\u001b[39m patients = pd.read_csv(os.path.join(BASE_DIR, \u001b[33m\"\u001b[39m\u001b[33mpatients.csv\u001b[39m\u001b[33m\"\u001b[39m))\n\u001b[32m---> \u001b[39m\u001b[32m18\u001b[39m diagnoses = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m.\u001b[49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mBASE_DIR\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mDIAGNOSES_ICD.csv\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     19\u001b[39m d_icd = pd.read_csv(os.path.join(BASE_DIR, \u001b[33m\"\u001b[39m\u001b[33mD_ICD_DIAGNOSES.csv\u001b[39m\u001b[33m\"\u001b[39m))\n\u001b[32m     21\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mAdmissions: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00madmissions.shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\akash\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m   1013\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m   1014\u001b[39m     dialect,\n\u001b[32m   1015\u001b[39m     delimiter,\n\u001b[32m   (...)\u001b[39m\u001b[32m   1022\u001b[39m     dtype_backend=dtype_backend,\n\u001b[32m   1023\u001b[39m )\n\u001b[32m   1024\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m-> \u001b[39m\u001b[32m1026\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\akash\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m    617\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m    619\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m parser = \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    622\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m    623\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\akash\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m   1617\u001b[39m     \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m   1619\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1620\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\akash\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m   1878\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m   1879\u001b[39m         mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1880\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1881\u001b[39m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1882\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1883\u001b[39m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1884\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcompression\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1885\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmemory_map\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1886\u001b[39m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1887\u001b[39m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding_errors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstrict\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1888\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstorage_options\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1889\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1890\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1891\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\akash\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\pandas\\io\\common.py:873\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m    868\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m    869\u001b[39m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m    870\u001b[39m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m    871\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m    872\u001b[39m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m         handle = \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m    874\u001b[39m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    875\u001b[39m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    876\u001b[39m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    877\u001b[39m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    878\u001b[39m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m    879\u001b[39m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    880\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    881\u001b[39m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m    882\u001b[39m         handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n",
      "\u001b[31mPermissionError\u001b[39m: [Errno 13] Permission denied: 'E:\\\\DL-BSP-SEMMM5\\\\data\\\\DIAGNOSES_ICD.csv'"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import shutil\n",
    "\n",
    "# -----------------------------\n",
    "# PATH CONFIGURATION\n",
    "# -----------------------------\n",
    "BASE_DIR = \"E:\\DL-BSP-SEMMM5\\data\"\n",
    "WAVEFORM_DIR = os.path.join(BASE_DIR, \"waveform_data\")\n",
    "FILTERED_DIR = os.path.join(BASE_DIR, \"mimic3_filtered_data\")\n",
    "os.makedirs(FILTERED_DIR, exist_ok=True)\n",
    "\n",
    "# -----------------------------\n",
    "# STEP 1 — LOAD CLINICAL DATA\n",
    "# -----------------------------\n",
    "admissions = pd.read_csv(os.path.join(BASE_DIR, \"admissions.csv\"))\n",
    "patients = pd.read_csv(os.path.join(BASE_DIR, \"patients.csv\"))\n",
    "diagnoses = pd.read_csv(os.path.join(BASE_DIR, \"DIAGNOSES_ICD.csv\"))\n",
    "d_icd = pd.read_csv(os.path.join(BASE_DIR, \"D_ICD_DIAGNOSES.csv\"))\n",
    "\n",
    "print(f\"Admissions: {admissions.shape}\")\n",
    "print(f\"Patients: {patients.shape}\")\n",
    "print(f\"Diagnoses: {diagnoses.shape}\")\n",
    "print(f\"ICD Master: {d_icd.shape}\")\n",
    "\n",
    "# -----------------------------\n",
    "# STEP 2 — FILTER CARDIOVASCULAR ICD CODES\n",
    "# -----------------------------\n",
    "# Cardiovascular diseases typically fall under ICD9 codes 390-459\n",
    "cardio_icd = d_icd[d_icd['ICD9_CODE'].astype(str).str.startswith(\n",
    "    tuple(str(i) for i in range(390, 460))\n",
    ")]\n",
    "cardio_codes = cardio_icd['ICD9_CODE'].unique()\n",
    "\n",
    "print(f\"Cardio ICD codes: {len(cardio_codes)}\")\n",
    "\n",
    "# -----------------------------\n",
    "# STEP 3 — GET ALL CARDIOVASCULAR ADMISSIONS\n",
    "# -----------------------------\n",
    "cardio_admissions = diagnoses[diagnoses['ICD9_CODE'].isin(cardio_codes)]\n",
    "cardio_subjects = cardio_admissions['SUBJECT_ID'].unique()\n",
    "\n",
    "print(f\"Total patients with cardiovascular issues: {len(cardio_subjects)}\")\n",
    "\n",
    "# -----------------------------\n",
    "# STEP 4 — MAP WAVEFORM FILES TO SUBJECTS\n",
    "# -----------------------------\n",
    "# Assuming waveform files are named like \"pXXXXXX\" (subject_id based folders)\n",
    "subject_folders = [f for f in os.listdir(WAVEFORM_DIR) if os.path.isdir(os.path.join(WAVEFORM_DIR, f))]\n",
    "\n",
    "print(f\"Total waveform folders: {len(subject_folders)}\")\n",
    "\n",
    "matched_subjects = []\n",
    "for folder in subject_folders:\n",
    "    try:\n",
    "        subject_id = int(folder.replace(\"p\", \"\"))\n",
    "        if subject_id in cardio_subjects:\n",
    "            matched_subjects.append(folder)\n",
    "    except:\n",
    "        continue\n",
    "\n",
    "print(f\"Matched subject folders: {len(matched_subjects)}\")\n",
    "\n",
    "# -----------------------------\n",
    "# STEP 5 — COPY ONLY MATCHED WAVEFORMS\n",
    "# -----------------------------\n",
    "for folder in matched_subjects:\n",
    "    src_path = os.path.join(WAVEFORM_DIR, folder)\n",
    "    dst_path = os.path.join(FILTERED_DIR, folder)\n",
    "    if not os.path.exists(dst_path):\n",
    "        shutil.copytree(src_path, dst_path)\n",
    "\n",
    "print(f\"✅ Filtered waveform data saved to: {FILTERED_DIR}\")\n"
   ]
  }
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